Full Analysis of Philippine Food prices from 2007 - 2025

Aims:

1. Compare average prices across different regions for the same food item and year.
2. Examine how prices of a specific food item have changed over time within a region.
3. Identify regional price variations for different food items.
4. Predict future food prices based on historical data.

from itables import init_notebook_mode
import pandas as pd
import numpy as np
from scipy.stats import shapiro
from scipy.stats import kruskal
import seaborn as sns
import statsmodels.api as sm
import matplotlib.pyplot as plt
import scipy.stats as stats
from scipy.stats import levene
from ipywidgets import interact
import dash
from dash import dcc, html, Input, Output
import plotly.express as px
import plotly.graph_objects as go
import scikit_posthocs as sp
import ipywidgets as widgets
from IPython.display import display, clear_output
from pmdarima import auto_arima
import warnings
import plotly.io as pio
from plotly.subplots import make_subplots

init_notebook_mode(all_interactive=True)
#open the file that was already cleaned, analyzed, and saved.
file_path = "../Data/Processed/eda_results.csv"
df = pd.read_csv(file_path)

Inspect the data

# View the first few rows of the dataset
df.head()
df

Region	Province	Food_Items	year	mean	median	Mode	Variance	Standard Deviation	IQR
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# View the columns and data types of the dataset
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 91200 entries, 0 to 91199
Data columns (total 10 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Region              91200 non-null  object 
 1   Province            91200 non-null  object 
 2   Food_Items          91200 non-null  object 
 3   year                91200 non-null  int64  
 4   mean                78875 non-null  float64
 5   median              78875 non-null  float64
 6   Mode                36432 non-null  float64
 7   Variance            76125 non-null  float64
 8   Standard Deviation  76125 non-null  float64
 9   IQR                 78875 non-null  float64
dtypes: float64(6), int64(1), object(3)
memory usage: 7.0+ MB

# View the summary statistics of the dataset though this may not make sense as they are already described in the EDA report
df.describe()

	year	mean	median	Mode	Variance	Standard Deviation	IQR
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#check the shape of the dataset
df.shape

(91200, 10)

# get the first and last readings for each food item
print(df['year'].agg(['min', 'max']))

min    2007
max    2025
Name: year, dtype: int64

For comparing average prices across regions for the same food item and year, the closing price is generally the best price to use. Here’s why:

1. Consistency: The closing price is considered the most representative of the market’s consensus for that period. It accounts for the entire trading session and reflects both supply and demand dynamics over time.
2. Standard Usage: The closing price is the most widely used price in financial markets, meaning data and analysis are typically focused around this price.
3. Simplicity: It eliminates the noise created by intra-day fluctuations, focusing on the price at the end of the trading session, which is more relevant for long-term comparisons.

# Filter the dataframe to keep only rows where 'food_item' starts with 'c_'
df_filtered = df[df['Food_Items'].str.startswith('c_')]

df_filtered

	Region	Province	Food_Items	year	mean	median	Mode	Variance	Standard Deviation	IQR
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df_filtered.loc[:, 'Food_Items'] = df_filtered['Food_Items'].str.replace('c_', '', regex=True)
df_filtered

	Region	Province	Food_Items	year	mean	median	Mode	Variance	Standard Deviation	IQR
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# Create figure
fig = go.Figure()

# Add traces for each food item and stat
food_items = df_filtered['Food_Items'].unique()
stats = ['mean', 'median']

for food in food_items:
    for stat in stats:
        df_food = df_filtered[df_filtered['Food_Items'] == food]
        for region in df_food['Region'].unique():
            df_region = df_food[df_food['Region'] == region]
            fig.add_trace(go.Scatter(
                x=df_region['year'],
                y=df_region[stat],
                mode='lines+markers',
                name=f"{food} - {region} ({stat})",
                visible=False
            ))

# Set initial visible traces (first food item, 'mean' stat)
initial_food = food_items[0]
initial_stat = 'mean'
for trace in fig.data:
    if initial_food in trace.name and initial_stat in trace.name:
        trace.visible = True

# Create separate dropdown buttons for food items and statistics
food_buttons = []
stat_buttons = []

for food in food_items:
    visibility = [food in trace.name and initial_stat in trace.name for trace in fig.data]
    food_buttons.append({
        'label': food,
        'method': 'update',
        'args': [{'visible': visibility},
                 {'title': f"{initial_stat.capitalize()} for {food} by Region and Year"}]
    })

for stat in stats:
    visibility = [initial_food in trace.name and stat in trace.name for trace in fig.data]
    stat_buttons.append({
        'label': stat,
        'method': 'update',
        'args': [{'visible': visibility},
                 {'yaxis': {'title': stat.capitalize()}},
                 {'title': f"{stat.capitalize()} for {initial_food} by Region and Year"}]
    })

# Update layout with separate dropdown menus
fig.update_layout(
    title=f"{initial_stat.capitalize()} for {initial_food} by Region and Year",
    xaxis_title="Year",
    yaxis_title=initial_stat.capitalize(),
    template="plotly_white",
    updatemenus=[
        {
            'buttons': food_buttons,
            'direction': 'down',
            'showactive': True,
            'x': 0,
            'xanchor': 'left',
            'y': 1.15,
            'yanchor': 'top',
        },
        {
            'buttons': stat_buttons,
            'direction': 'down',
            'showactive': True,
            'x': 0.26,
            'xanchor': 'left',
            'y': 1.15,
            'yanchor': 'top',
        }
    ]
)

# Show figure
fig.show()

The graph shows the mean (average) price or median price of different food items over time for different regions in the Philippines. #### Key Observation 1. Trend - Generally, the price of all food items has been increasing over the years across most regions. There are some fluctuations and periods of stability for some items, but the overall trend is upward. 2. Regional Differences - There is significant variation in price of different food items across regions. Some regions consistently have higher prices than others. Price fluctuations also varies between regions. Some regions experience more volatile prices than others.

Factors like weather patterns, government policies, agriculture changes, food transportation and global market trends can influence these prices.

To know which regions has the highest and lowest price every year for every food item, max, min will be calculated. Then range is next to know how wide the difference between the highest and lowest price.

file_path = "../Data/Interim/cleaned_food_prices.csv"
df_range = pd.read_csv(file_path)

#dropping columns related to food price index
df_nofpi_range = df_range.drop(columns=['o_food_price_index', 'h_food_price_index', 'l_food_price_index', 'c_food_price_index', 'inflation_food_price_index', 'trust_food_price_index'])

# Convert 'Date' column to datetime format
df_nofpi_range['Date'] = pd.to_datetime(df_nofpi_range['Date'])

#dropping columns related to inflation
df_noinf_range = df_nofpi_range.drop(columns=['inflation_beans','inflation_cabbage', 'inflation_carrots', 'inflation_eggs', 'inflation_garlic', 'inflation_meat_beef_chops', 'inflation_meat_chicken_whole', 'inflation_meat_pork', 'inflation_onions', 'inflation_potatoes', 'inflation_rice', 'inflation_tomatoes'])

#dropping columns related to trust scores
df_cleaned_range = df_noinf_range.drop(columns=['trust_beans','trust_cabbage', 'trust_carrots', 'trust_eggs', 'trust_garlic', 'trust_meat_beef_chops', 'trust_meat_chicken_whole', 'trust_meat_pork', 'trust_onions', 'trust_potatoes', 'trust_rice', 'trust_tomatoes'])

#dropping uneeded columns
df_unneeded_range = df_cleaned_range.drop(columns=['country', 'City', 'lat', 'lon', 'Province', 'year', 'month'])

# Reshaping from wide to long format (including year and month as part of the identifier)
df_range = df_unneeded_range.melt(id_vars=['Region', 'Date'], var_name='Food_Items', value_name='Price')

df_range_filtered = df_range[df_range['Food_Items'].str.startswith('c_')]

df_range_filtered.loc[:, 'Food_Items'] = df_range_filtered['Food_Items'].str.replace('c_', '', regex=True)

price_stats = df_range_filtered.groupby(['Date', 'Food_Items'])['Price'].agg(['min', 'max', 'mean']).reset_index()
price_stats

Date	Food_Items	min	max	mean
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def range_chart(price_stats):
    # Convert Date to datetime if it isn't already
    price_stats['Date'] = pd.to_datetime(price_stats['Date'])
    
    # Get unique food items for our dropdown
    food_items = sorted(price_stats['Food_Items'].unique())
    
    # Initialize our figure
    fig = go.Figure()
    
    # Create a list to store our dropdown menu options
    dropdown_buttons = []
    
    # For each food item, we'll create a filled range plot
    for i, food_item in enumerate(food_items):
        # Filter data for this specific food item
        item_data = price_stats[price_stats['Food_Items'] == food_item]
        
        # Create a filled range plot
        fig.add_trace(
            go.Scatter(
                x=item_data['Date'],
                y=item_data['max'],
                name='Price Range',
                mode='lines',
                line=dict(width=0),
                showlegend=False,
                visible=(i == 0)  # Only first item visible initially
            )
        )
        
        fig.add_trace(
            go.Scatter(
                x=item_data['Date'],
                y=item_data['min'],
                name=f'{food_item} Price Range',
                fill='tonexty',  # Fill area between traces
                mode='lines',
                line=dict(width=0),
                fillcolor='rgba(68, 138, 255, 0.4)',  # Semi-transparent blue
                visible=(i == 0)  # Only first item visible initially
            )
        )
        
        # Add a line for the mean price
        fig.add_trace(
            go.Scatter(
                x=item_data['Date'],
                y=item_data['mean'],
                name='Average Price',
                mode='lines',
                line=dict(
                    color='rgb(31, 119, 180)',
                    width=2
                ),
                visible=(i == 0)  # Only first item visible initially
            )
        )
        
        # Create visibility list for this food item's traces
        # Each food item has 3 traces (upper bound, lower bound, and mean)
        visible_list = [False] * (len(food_items) * 3)
        visible_list[i*3:(i*3)+3] = [True, True, True]
        
        # Add button for this food item to dropdown menu
        dropdown_buttons.append(
            dict(
                args=[{"visible": visible_list},
                      {"title": f"Price Range for {food_item} Over Time"}],
                label=food_item,
                method="update"
            )
        )
    
    # Update layout with dropdown menu and other customizations
    fig.update_layout(
        updatemenus=[
            dict(
                buttons=dropdown_buttons,
                direction="down",
                showactive=True,
                x=0.1,
                y=1.15,
                xanchor="left",
                yanchor="top"
            )
        ],
        # Add title and axis labels
        title=f"Price Range for {food_items[0]} Over Time",
        xaxis_title="Date",
        yaxis_title="Price",
        hovermode='x unified',
        # Improve layout appearance
        template="plotly_white",
        margin=dict(t=100),  # Add top margin for dropdown
        # Improve legend layout
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        )
    )
    
    # Customize hover template to show min, max, and mean prices
    for trace in fig.data:
        trace.hovertemplate = '%{y:.2f}<extra></extra>'
    
    return fig

# Example usage:
fig = range_chart(price_stats)
fig.show()

price_stats['price_spread'] = price_stats['max'] - price_stats['min']

def spread_chart(price_stats):
    
    # Get unique food items for dropdown
    food_items = sorted(price_stats['Food_Items'].unique())
    
    # Initialize figure
    figrange = go.Figure()
    
    # Create a list to store dropdown menu options
    dropdown_buttons = []
    
    for i, food_item in enumerate(food_items):
        # Filter data for this food item
        item_data = price_stats[price_stats['Food_Items'] == food_item]
        
        # Create the main line plot for price spread
        figrange.add_trace(
            go.Scatter(
                x=item_data['Date'],
                y=item_data['price_spread'],
                name='Price Spread',
                mode='lines+markers',
                line=dict(color='blue', width=2),
                marker=dict(size=6),
                visible=(i == 0),
                customdata=np.stack((
                    item_data['min'],
                    item_data['max'],
                    item_data['mean']
                ), axis=-1),
                hovertemplate=(
                    '<b>Date</b>: %{x|%B %Y}<br>' +
                    '<b>Price Spread</b>: ₱%{y:.2f}<br>' +
                    '<b>Minimum Price</b>: ₱%{customdata[0]:.2f}<br>' +
                    '<b>Maximum Price</b>: ₱%{customdata[1]:.2f}<br>' +
                    '<b>Average Price</b>: ₱%{customdata[2]:.2f}<br>' +
                    '<extra></extra>'
                )
            )
        )
        
        # Calculate statistics for annotations
        avg_spread = item_data['price_spread'].mean()
        max_spread = item_data['price_spread'].max()
        max_spread_date = item_data.loc[item_data['price_spread'].idxmax(), 'Date']
        
        # Add a dashed line for average spread
        figrange.add_trace(
            go.Scatter(
                x=[item_data['Date'].min(), item_data['Date'].max()],
                y=[avg_spread, avg_spread],
                name='Average Spread',
                mode='lines',
                line=dict(color='red', width=2, dash='dash'),
                visible=(i == 0)
            )
        )
        
        # Create visibility list for this food item's traces
        visible_list = [False] * (len(food_items) * 2)
        visible_list[i*2:(i*2)+2] = [True, True]
        
        # Add button for this food item to dropdown menu
        dropdown_buttons.append(
            dict(
                args=[
                    {"visible": visible_list},
                    {
                        "title": f"Price Spread Analysis for {food_item}<br>"
                    }
                ],
                label=food_item,
                method="update"
            )
        )
    
    # Update layout
    figrange.update_layout(
        updatemenus=[
            dict(
                buttons=dropdown_buttons,
                direction="down",
                showactive=True,
                x=0.1,
                y=1.15,
                xanchor="left",
                yanchor="top"
            )
        ],
        title=f"Price Spread Analysis for {food_items[0]}",
        xaxis_title="Date",
        yaxis_title="Price Spread in ₱",
        template="plotly_white",
        margin=dict(t=120, b=50),  # Increased top margin for subtitle
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        # Add annotation
        annotations=[
            dict(
                text="Price Spread = Maximum Price - Minimum Price",
                xref="paper",
                yref="paper",
                x=0,
                y=-0.15,
                showarrow=False,
                font=dict(size=10)
            )
        ]
    )
    
    return figrange

figrange = spread_chart(price_stats)
figrange.show()

Key Observations: 1. Overall Trend - Both the minimum and maximum prices show a general upward trend over the years, indicating an overall increase in the price of every food items. Some even recorded highest price increase on 2020, likely due to the COVID-19 pandemic and its impact on supply chains. 2. Price Range - The gap between the minimum and maximum prices for most items widens significantly in 2020 and 2021. This suggests that there were greater variations in prices across regions during these years.

Posssible causes: 1. COVID-19 Pandemic - The pandemic disrupted supply chains and increased demand for certain food items, which could have led to price volatility. 2. Regional Differences - Differences in the impact of the pandemic on regional economies and agricultural production could have contributed to price variations.

To truly know if the regions really have price difference over the years, hypothesis testing must be done. Please refer to .._testing.ipynb to know why Kruskall-Wallis was used here.

#open the csv file that was already analyzed with kruskall wallis test.
hypothesis_results = "../Data/Processed/hypothesis_testing_result.csv"
hyp_df = pd.read_csv(hypothesis_results)

# View the first few rows of the dataset
print(hyp_df.head())

   Year  Food_Item  H-statistic        p-value           Result
0  2007      beans          NaN            NaN  Not Significant
1  2007    c_beans  1077.368029  2.491117e-218      Significant
2  2007  c_cabbage  1105.762042  2.066749e-224      Significant
3  2007  c_carrots  1093.290901  9.693593e-222      Significant
4  2007     c_eggs   905.101619  1.726764e-181      Significant

# Filter the dataframe to keep only rows where 'food_item' starts with 'c_'
hyp_df_filtered = hyp_df[hyp_df['Food_Item'].str.startswith('c_')]

print(hyp_df_filtered)

      Year    Food_Item  H-statistic        p-value       Result
1     2007      c_beans  1077.368029  2.491117e-218  Significant
2     2007    c_cabbage  1105.762042  2.066749e-224  Significant
3     2007    c_carrots  1093.290901  9.693593e-222  Significant
4     2007       c_eggs   905.101619  1.726764e-181  Significant
5     2007     c_garlic   393.719714   3.807277e-73  Significant
...    ...          ...          ...            ...          ...
1088  2025  c_meat_pork    44.757215   2.646148e-04  Significant
1089  2025     c_onions   101.512409   4.662566e-14  Significant
1090  2025   c_potatoes    90.632301   4.683642e-12  Significant
1091  2025       c_rice    67.209085   6.484158e-08  Significant
1092  2025   c_tomatoes    70.027332   2.133517e-08  Significant

[228 rows x 5 columns]

# removing prefix _c in the food items
hyp_df_filtered.loc[:, 'Food_Item'] = hyp_df_filtered['Food_Item'].str.replace('c_', '', regex=True)
print(hyp_df_filtered)

      Year  Food_Item  H-statistic        p-value       Result
1     2007      beans  1077.368029  2.491117e-218  Significant
2     2007    cabbage  1105.762042  2.066749e-224  Significant
3     2007    carrots  1093.290901  9.693593e-222  Significant
4     2007       eggs   905.101619  1.726764e-181  Significant
5     2007     garlic   393.719714   3.807277e-73  Significant
...    ...        ...          ...            ...          ...
1088  2025  meat_pork    44.757215   2.646148e-04  Significant
1089  2025     onions   101.512409   4.662566e-14  Significant
1090  2025   potatoes    90.632301   4.683642e-12  Significant
1091  2025       rice    67.209085   6.484158e-08  Significant
1092  2025   tomatoes    70.027332   2.133517e-08  Significant

[228 rows x 5 columns]

year_dropdown = widgets.Dropdown(
    options=hyp_df_filtered["Year"].unique(),
    value=hyp_df_filtered["Year"].min(),
    description="Select Year:",
    style={'description_width': 'initial'}
)

# Output widget to display the table
output = widgets.Output()

def update_table(change):
    with output:
        output.clear_output()
        display(hyp_df_filtered[hyp_df_filtered["Year"] == year_dropdown.value])

# Trigger update on change
year_dropdown.observe(update_table, names="value")

# Display widgets
display(year_dropdown, output)

# Initial Table Display
update_table(None)

# or you can just count the number of unique Results
hyp_df_filtered["Result"].value_counts()

	count
Result
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This means all the items for all the regions over the year have different prices. But which regions have different prices?

df_d = df_cleaned_range.drop(columns=['country', 'City', 'lat', 'lon', 'Province', 'Date', 'month'])

df_dt = df_d.melt(id_vars=['Region', 'year'], var_name='Food_Items', value_name='Price')
df_dt = df_dt[df_dt['Food_Items'].str.startswith('c_')]
df_dt.loc[:, 'Food_Items'] = df_dt['Food_Items'].str.replace('c_', '', regex=True)
df_dt

	Region	year	Food_Items	Price
Loading ITables v2.2.4 from the init_notebook_mode cell... (need help?)

# Perform Dunn's Test for all food items and years
dt_results = []
for item in df_dt['Food_Items'].unique():
    for year in df_dt['year'].unique():
        filter_dfdt = df_dt[(df_dt['Food_Items'] == item) & (df_dt['year'] == year)]
        if not filter_dfdt.empty:
            dunn_result = sp.posthoc_dunn(filter_dfdt, val_col='Price', group_col='Region')
            dunn_result['Food_Items'] = item
            dunn_result['year'] = year
            dt_results.append(dunn_result)

# Combine results into a single DataFrame and save to CSV
if dt_results:
    final_results = pd.concat(dt_results)
    final_results.to_csv("dunns_test_results.csv", index=True)
    print("Dunn's test results saved to dunns_test_results.csv")
else:
    print("No valid comparisons found.")

Dunn's test results saved to dunns_test_results.csv

# Read the CSV file
df = pd.read_csv('dunns_test_results.csv', index_col=0)

food_items = sorted(df['Food_Items'].dropna().unique())
years = sorted(df['year'].unique())

# Create initial heatmap data
def get_heatmap_data(food_item, year):
    """Filters the dataset and returns heatmap data."""
    filtered_data = df[(df['Food_Items'] == food_item) & (df['year'] == year)]
    
    if filtered_data.empty:
        return None, None, None  # No data available case

    heatmap_data = filtered_data.drop(columns=['Food_Items', 'year'])
    return heatmap_data.values, heatmap_data.columns, heatmap_data.index

# Initialize first heatmap
initial_food = food_items[0]
initial_year = years[0]
z_values, x_labels, y_labels = get_heatmap_data(initial_food, initial_year)

# Create figure
fig = go.Figure()

# Add initial heatmap
fig.add_trace(go.Heatmap(
    z=z_values,
    x=x_labels,
    y=y_labels,
    colorscale='rdylgn',
    colorbar=dict(title='p-value',
                  x=1.2,
                  titleside='top',
                  len=0.5
                 ),
    hoverongaps=False
))

# Update layout
fig.update_layout(
    title=dict(text=f"Dunn's Test Results: {food} ({initial_year})", 
                          font=dict(size=20),
                          y=0.98,
                          x=0.5,
                          ),
    height=620,
    width=800,
    margin=dict(l=10, r=10, t=25, b=1),
    xaxis=dict(tickangle=45, title='Region', constrain='domain'),
    yaxis=dict(title='Region', constrain='domain', scaleanchor='x', scaleratio=1)
)

# Add dropdowns for interactivity
dropdown_food = [
    {
        "label": food,
        "method": "update",
        "args": [
            {"z": [get_heatmap_data(food, initial_year)[0]]},  # Update heatmap
            {"title":dict(text=f"Dunn's Test Results: {food} ({initial_year})", 
                          font=dict(size=20),
                          y=0.98,
                          x=0.5,
                          )} 
        ]
    }
    for food in food_items
]

dropdown_year = [
    {
        "label": str(year),
        "method": "update",
        "args": [
            {"z": [get_heatmap_data(initial_food, year)[0]]},  # Update heatmap
            {"title":dict(text=f"Dunn's Test Results: {food} ({initial_year})", 
                          font=dict(size=20),
                          y=0.98,
                          x=0.5,
                          )} 
        ]
    }
    for year in years
]

fig.update_layout(
    updatemenus=[
        {
            "buttons": dropdown_food,
            "direction": "down",
            "showactive": True,
            "x": -1,
            "xanchor": "left",
            "y": 0.99,
            "yanchor": "top",
            "active": 0,
            "pad": {"r": 0.5, "t": 0.5},
            "name": "Food Item",
        },
        {
            "buttons": dropdown_year,
            "direction": "down",
            "showactive": True,
            "x": -0.47,
            "xanchor": "left",
            "y": 0.99,
            "yanchor": "top",
            "active": 0,
            "pad": {"r": 0.5, "t": 0.5},
            "name": "Year",
        }
    ]
)

fig.show()

This heatmap visualizes the results of a Dunn’s post-hoc test for significant differences in prices of different food items between regions across the years.

The color scale indicates the following: * Red (Close to 0): Indicates a statistically significant difference in bean prices between two regions. * Light Green to Yellow (Around 0.2 to 0.8): Suggests a possible difference, but not statistically significant based on the chosen significance level (typically 0.05) * Dark Green (Close to 0): Indicates no statistically significant difference in bean prices between the two regions.

Key Observations

1. Significant Differences Exist: The presence of so many red cells indicates that there are statistically significant differences in food prices in almost all regions at any given years.
2. No Significant Differences: patches of green indicate pairs of regions where the Dunn’s test did not find a statistically significant difference in food prices, but there are only few of them.
3. Market Average Comparison: The “Market Average” row/column allows you to see how each region’s prices compare to the overall average. Dark red cells in this row/column would indicate regions with prices significantly different from the market average.

Food price Prediction 3 years into the future

#dropping uneeded columns
df_unneeded_range = df_cleaned_range.drop(columns=['country', 'City', 'lat', 'lon', 'Province', 'year', 'month'])

# Reshaping from wide to long format (including year and month as part of the identifier)
df_range = df_unneeded_range.melt(id_vars=['Region', 'Date'], var_name='Food_Items', value_name='Price')

df_range_filtered = df_range[df_range['Food_Items'].str.startswith('c_')]

df_range_filtered.loc[:, 'Food_Items'] = df_range_filtered['Food_Items'].str.replace('c_', '', regex=True)

# Sort by date
df = df_range_filtered.sort_values(by=['Region', 'Date'])

# Suppress specific warnings
warnings.filterwarnings("ignore", category=UserWarning, module="sklearn")
warnings.filterwarnings("ignore", category=FutureWarning, module="sklearn")
warnings.filterwarnings("ignore", category=FutureWarning, module="pmdarima")
warnings.filterwarnings("ignore", category=DeprecationWarning, module="pmdarima")


# Convert date column to datetime
df['Date'] = pd.to_datetime(df['Date'])

# Sort by date
df = df.sort_values(by=['Region', 'Date'])

# Dropdown for interactive visualization
regions = df['Region'].unique()
items = df['Food_Items'].unique()

region_dropdown = widgets.Dropdown(options=regions, description='Region:')
item_dropdown = widgets.Dropdown(options=items, description='Food Item:')

def preprocess_data(region, item):
    region_df = df[(df['Region'] == region) & (df['Food_Items'] == item)]
    region_df = (
        region_df.groupby('Date', as_index=False)['Price']
        .mean()  # Aggregate duplicate dates by averaging
    )
    region_df = region_df.set_index('Date').asfreq('MS')  # Ensure monthly frequency
    region_df['Price'] = region_df['Price'].interpolate()  # Fill missing values
    return region_df

def update_graph(region, item):
    region_df = preprocess_data(region, item)

    if len(region_df) < 12:
        print(f"Not enough data for {region} - {item} to forecast.")
        return

    # Use AutoARIMA to find the best model
    model = auto_arima(region_df['Price'], seasonal=True, m=12, stepwise=True, trace=True, max_order=None, stationary=False)
    
    # Forecast the next 36 months
    forecast = model.predict(n_periods=36)

    # Plot the historical data and forecast
    plt.figure(figsize=(12, 6))
    plt.plot(region_df.index, region_df['Price'], label='Historical Prices')
    plt.plot(pd.date_range(region_df.index[-1], periods=37, freq='MS')[1:], forecast, label='Forecast', linestyle='dashed', color='red')
    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.legend()
    plt.title(f'Price Forecast for {region} - {item} (Next 36 Months)')
    plt.show()

interactive_plot = widgets.interactive(update_graph, region=region_dropdown, item=item_dropdown)
display(interactive_plot)

Key Observations

1. Historical Price Volatility: The blue line representing historical prices shows significant fluctuations, especially noticeable spikes around 2008 and smaller ones in other years. This indicates that most food prices have been subject to considerable volatility over the past two decades.
2. Recent Price Increase: There’s a clear upward trend in food prices in the period leading up to the forecast (roughly from 2020 to 2024). This suggests recent factors have been pushing prices higher.
3. Relatively Stable Forecast: The red dashed line, representing the forecast for the next 36 months, shows a relatively stable and slightly upward trend. The model predicts a continued but moderated increase compared to the recent past.

Key Observations

1. Historical Volatility:
   • Weather Patterns:adverse weather events in key agricultural regions can significantly impact supply and cause price volatility.
   • Changes in Demand: Fluctuations in global demand, driven by population growth or economic factors, can also contribute to price swings.
2. Recent Price Increase:
   • Inflation: General inflation in the economy could be pushing up agricultural commodity prices.
   • Supply Chain Issues: Disruptions to global supply chains (e.g., due to pandemics, conflicts, or trade policies) can lead to shortages and price increases.
   • Increased Input Costs: Rising costs of fertilizers, fuel, and labor can increase the cost of food production, which could be passed on to consumers.
3. Relatively Stable Forecast:
   • Model Assumption: ARIMA models assume that, to some extent, future trends will resemble past patterns. The stable forecast suggests the model has not picked up any strong upward or downward pressures for the next 36 months beyond what is already captured in the recent trend.